Although the benefits of physical activity (PA) are well known, physical inactivity is highly prevalent among people with obesity. The objective of this systematic review was to i) appraise knowledge ...on PA motives, barriers, and preferences in individuals with obesity, and ii) quantify the most frequently reported PA motives, barriers and preferences in this population. Six databases (Pubmed, CINAHL, Psyarticle, SportDiscus, Web of science and Proquest) were searched by independent reviewers to identify relevant quantitative or qualitative articles reporting PA motives, barriers or preferences in adults with body mass index greater than or equal to 30 kg/m.sup.2 (last searched in June 2020). Risk of bias for each study was assessed by two independent reviewers with the Mixed Methods Appraisal Tool (MMAT). From 5,899 papers identified, a total of 27 studies, 14 quantitative, 10 qualitative and 3 mixed studies were included. About 30% of studies have a MMAT score below 50% (k = 8). The three most reported PA motives in people with obesity were weight management, energy/physical fitness, and social support. The three most common PA barriers were lack of self-discipline/motivation, pain or physical discomfort, and lack of time. Based on the only 4 studies available, walking seems to be the preferred mode of PA in people with obesity. Weight management, lack of motivation and pain are key PA motives and barriers in people with obesity, and should be addressed in future interventions to facilitate PA initiation and maintenance. Further research is needed to investigate the PA preferences of people with obesity.
Carbonization of Phoenix dactylifera L stones followed by microwave K2CO3 activation was adopted for preparation of granular activated carbon (KAC). High yield and favorable pore characteristics in ...terms of surface area and pore volume were reported for KAC as follows: 44%, 852m2/g, and 0.671cm3/g, respectively. The application of KAC as adsorbent for attraction of ciprofloxacin (CIP) and norfloxacin (NOR) was investigated using fixed bed systems. The effect of flow rate (0.5–1.5ml/min), bed height (15–25cm), and initial drug concentration (75–225mg/l) on the behavior of breakthrough curves was explained. The fixed bed analysis showed the better correlation of breakthrough data by both Thomas and Yoon-Nelson models. Inlet drug concentration was of greatest effect on breakthrough data compared to other fixed bed variables. Experimental and calculated breakthrough data were obtained for CIP and NOR adsorption on KAC, thus being important for design of fixed bed column.
•K2CO3 activated carbon (KAC) was prepared from date stones.•Ciprofloxacin (CIP) and norfloxacin (NOR) were adsorbed on KAC.•Effects of fixed bed variables on breakthrough data were illustrated.•Breakthrough data were significantly affected by inlet drug concentration.•Thomas and Yoon–Nelson models were appropriate for data analysis.
Our understanding of gene regulation is constantly evolving. It is now clear that the majority of cellular transcripts are non-coding RNAs. The spectrum of non-coding RNAs is diverse and includes ...short (<200 nt) and long non-coding RNAs (lncRNAs) (>200 nt). LncRNAs regulate gene expression through diverse mechanisms. In this review, we describe the emerging roles of lncRNA mediated plant gene regulation. We discuss the current classification of lncRNAs and their role in genome organization and gene regulation. We also highlight a subset of lncRNAs that are epigenetic regulators of plant gene expression. Lastly, we provide an overview of emerging techniques and databases that are employed for the identification and characterization of plant lncRNAs.
•New proposed pipeline of pre-processing step and CNN for retinal vessels segmentation.•A deep strided-CNN is proposed to better and faster segment retinal vessels.•Skip connections are used to ...generate sharper and well segmented vessels.•The selected loss function fulfils the problem requirement and the nature of the dataset.
In this paper, a deep convolutional neural network (CNN) is proposed for accurate segmentation of retinal blood vessels. This method plays a significant role in observing many eye diseases. A strided-CNN model is proposed for accurate segmentation of retinal vessels, especially the tiny vessels. The model is a fully convolutional model consisting of an encoder part and a decoder part where the pooling layers are replaced with strided convolutional layers. The strided convolutional layer approach was chosen over the pooling layers approach as the former can be trained. The morphological mappings along with the Principal Component Analysis (PCA)- based pre-processing steps are used to generate contrast images for training dataset. Skip connections are implemented to concatenate features from the encoder part and the decoder part to enhance the vessels segmentation especially the tiny vessels and to make the vessel’s edges sharper. We used a class balancing loss function to train and optimize the proposed model to improve vessel image quality. The impact of the proposed segmentation method is evaluated on four databases namely DRIVE, STARE, CHASE-DB1 and HRF. Overall model performance, particularly with respect to tiny vessels, is primarily influenced by sensitivity and accuracy metrics. We demonstrate that our model outperforms other models with a sensitivity of 0.87, 0.808, 0.886 and 0.829 on DRIVE, STARE, CHASE_DB1 and HRF respectively, along with respective accuracies of 0.956, 0.954, 0.976 and 0.962.
This paper presents a comprehensive review of the principle and application of deep learning in retinal image analysis. Many eye diseases often lead to blindness in the absence of proper clinical ...diagnosis and medical treatment. For example, diabetic retinopathy (DR) is one such disease in which the retinal blood vessels of human eyes are damaged. The ophthalmologists diagnose DR based on their professional knowledge, that is labor intensive. With the advances in image processing and artificial intelligence, computer vision-based techniques have been applied rapidly and widely in the field of medical images analysis and are becoming a better way to advance ophthalmology in practice. Such approaches utilize accurate visual analysis to identify the abnormality of blood vessels with improved performance over manual procedures. More recently, machine learning, in particular, deep learning, has been successfully implemented in this area. In this paper, we focus on recent advances in deep learning methods for retinal image analysis. We review the related publications since 1982, which include more than 80 papers for retinal vessels detections in the research scope spanning from segmentation to classification. Although deep learning has been successfully implemented in other areas, we found only 17 papers so far focus on retinal blood vessel segmentation. This paper characterizes each deep learning based segmentation method as described in the literature. Analyzing along with the limitations and advantages of each method. In the end, we offer some recommendations for future improvement for retinal image analysis.
Magnetic Resonance Imaging (MRI) has commonly been used to detect and diagnose brain disease and monitor treatment as non-invasive imaging technology. MRI produces three-dimensional images that help ...neurologists to identify anomalies from brain images precisely. However, this is a time-consuming and labor-intensive process. The improvement in machine learning and efficient computation provides a computer-aid solution to analyze MRI images and identify the abnormality quickly and accurately. Image segmentation has become a hot and research-oriented area in the medical image analysis community. The computer-aid system for brain abnormalities identification provides the possibility for quickly classifying the disease for early treatment. This article presents a review of the research papers (from 1998 to 2020) on brain tumors segmentation from MRI images. We examined the core segmentation algorithms of each research paper in detail. This article provides readers with a complete overview of the topic and new dimensions of how numerous machine learning and image segmentation approaches are applied to identify brain tumors. By comparing the state-of-the-art and new cutting-edge methods, the deep learning methods are more effective for the segmentation of the tumor from MRI images of the brain.
► Activated carbon was prepared from date pits by ferric chloride activation (FAC). ► FAC was used to remove methylene blue (MB) from aqueous solutions. ► Surface area of 780.06m2/g was characterized ...for FAC. ► Maximum MB capacity of 259.25mg/g was reported using Sips isotherm. ► The kinetic data were well described by pseudo-second order model.
Ferric chloride has been utilized as an activator for preparation of activated carbon from an agricultural solid waste, date pits. The characteristics of prepared activated carbon (FAC) were determined and found to have a surface area and iodine number of 780.06m2/g and 761.40mg/g, respectively. Experiments were carried out to evaluate the batch adsorption isotherms and kinetics of methylene blue (MB) on FAC at different temperatures. Experimental equilibrium data were analyzed by the Langmuir, Freundlich and Sips isotherm models. The results show that the best fit was achieved with the Sips isotherm equation with a maximum MB adsorption capacity of 259.25mg/g. Pseudo-first order, pseudo-second order and intraparticle diffusion models were used to analyze the kinetic data obtained at different initial MB concentrations. The adsorption kinetic data were well described by the pseudo-second order model. The calculated thermodynamic parameters, namely ΔG, ΔH, and ΔS showed that adsorption of MB onto date pits was spontaneous and endothermic under examined conditions.
•Minimum inhibitory concentrations (MIC) are determined for S. pasteurii with a range of metals.•Zinc & cadmium bioprecipitation is strongly linked to microbial carbonate generation.•Lead & copper ...carbonate bioprecipitation is limited & abiotic processes may be significant.•Bioprecipitation allows survival at & remediation of higher metal concentrations than expected.
Biological precipitation of metallic contaminants has been explored as a remedial technology for contaminated groundwater systems. However, metal toxicity and availability limit the activity and remedial potential of bacteria. We report the ability of a bacterium, Sporosarcina pasteurii, to remove metals in aerobic aqueous systems through carbonate formation. Its ability to survive and grow in increasingly concentrated aqueous solutions of zinc, cadmium, lead and copper is explored, with and without a metal precipitation mechanism. In the presence of metal ions alone, bacterial growth was inhibited at a range of concentrations depending on the metal. Microbial activity in a urea-amended medium caused carbonate ion generation and pH elevation, providing conditions suitable for calcium carbonate bioprecipitation, and consequent removal of metal ions. Elevation of pH and calcium precipitation are shown to be strongly linked to removal of zinc and cadmium, but only partially linked to removal of lead and copper. The dependence of these effects on interactions between the respective metal and precipitated calcium carbonate are discussed. Finally, it is shown that the bacterium operates at higher metal concentrations in the presence of the urea-amended medium, suggesting that the metal removal mechanism offers a defence against metal toxicity.
Missense mutations in p53 are severely deleterious and occur in over 50% of all human cancers. The majority of these mutations are located in the inherently unstable DNA-binding domain (DBD), many of ...which destabilize the domain further and expose its aggregation-prone hydrophobic core, prompting self-assembly of mutant p53 into inactive cytosolic amyloid-like aggregates. Screening an oligopyridylamide library, previously shown to inhibit amyloid formation associated with Alzheimer's disease and type II diabetes, identified a tripyridylamide, ADH-6, that abrogates self-assembly of the aggregation-nucleating subdomain of mutant p53 DBD. Moreover, ADH-6 targets and dissociates mutant p53 aggregates in human cancer cells, which restores p53's transcriptional activity, leading to cell cycle arrest and apoptosis. Notably, ADH-6 treatment effectively shrinks xenografts harboring mutant p53, while exhibiting no toxicity to healthy tissue, thereby substantially prolonging survival. This study demonstrates the successful application of a bona fide small-molecule amyloid inhibitor as a potent anticancer agent.
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